RandomNet

class RandomNet(pars=None, key_dict=None, **kwargs)

Bases: DynamicNetwork

Random connectivity between agents

Attributes

beta	Relative transmission on each network edge
members	Return sorted array of all members
now	Shortcut to self.t.now()
p1	The first half of a network edge (person 1)
p2	The second half of a network edge (person 2)
states	Return a flat list of all states
statesdict	Return a flat dictionary (objdict) of all states
ti	Get the current module timestep
timevec	Shortcut to self.t.timevec

Methods

static get_source(inds, n_contacts)

Optimized helper function for getting contacts

get_edges(inds, n_contacts)

Efficiently find edges

Note that because of the shuffling operation, each person is assigned 2N contacts (i.e. if a person has 5 contacts, they appear 5 times in the ‘source’ array and 5 times in the ‘target’ array). Therefore, the number_of_contacts argument to this function should be HALF of the total contacts a person is expected to have, if both the source and target array outputs are used (e.g. for social contacts)

adjusted_number_of_contacts = np.round(number_of_contacts / 2).astype(ss.dtype.int)

Whereas for asymmetric contacts (e.g. staff-public interactions) it might not be necessary

Parameters:

• inds – List/array of person indices

• number_of_contacts – List/array the same length as inds with the number of unidirectional

• Therefore (contacts to assign to each person.)

• number (a person will have on average TWICE this)

• contacts. (of random)

Returns: Two arrays, for source and target

add_pairs()

Generate edges